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AI isn’t replacing maintenance teams in oil and gas. It’s giving the best teams a faster way to see trouble coming—and a cleaner way to keep plants running.
This book does not treat AI as a magic button. It shows how AI supports disciplined maintenance by improving what matters most: documentation, scheduling decisions, early warning “flags,” and reliability tracking. When your work orders are done correctly and on time, AI systems can learn what “normal” looks like and alert you before small problems become shutdowns. When documentation is sloppy or delayed, false alarms and missed risks follow. This guide shows you how to build the clean maintenance history that makes predictive tools accurate and useful.
Inside you’ll learn how to:
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Identify and prioritize critical assets in oil and gas operations
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Build realistic task frequencies using OEM guidance, field history, and operating conditions
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Plan and execute work to minimize downtime and coordinate safe shutdown windows
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Set up inventory so critical spares are available without wasting money on overstock
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Track the right KPIs (MTBF, MTTR, preventive vs. reactive ratio, cost drivers) and use them to improve decisions
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Understand how AI fits into the workflow—where the “algorithm” generates flags and where the CMMS work order process begins and ends
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Keep the program alive long-term through audits, training habits, and accountability
Written in clear language for engineers, supervisors, planners, and technicians, this guide is designed to be used—not just read. If you want fewer breakdowns, lower maintenance costs, better uptime, and a maintenance program your team can run with confidence, this book gives you a proven roadmap—updated for the AI era.
By Lidia LoPinto, MChE
Chemical Engineer and former CEO of a maintenance software company